Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory496.6 MiB
Average record size in memory520.7 B

Variable types

Categorical14
Numeric18

Alerts

device_fraud_count has constant value "0"Constant
bank_branch_count_8w is highly overall correlated with bank_months_countHigh correlation
bank_months_count is highly overall correlated with bank_branch_count_8wHigh correlation
credit_risk_score is highly overall correlated with proposed_credit_limitHigh correlation
current_address_months_count is highly overall correlated with prev_address_months_countHigh correlation
month is highly overall correlated with velocity_24h and 1 other fieldsHigh correlation
prev_address_months_count is highly overall correlated with current_address_months_countHigh correlation
proposed_credit_limit is highly overall correlated with credit_risk_scoreHigh correlation
velocity_24h is highly overall correlated with month and 1 other fieldsHigh correlation
velocity_4w is highly overall correlated with month and 1 other fieldsHigh correlation
fraud_bool is highly imbalanced (91.2%)Imbalance
employment_status is highly imbalanced (51.2%)Imbalance
foreign_request is highly imbalanced (83.0%)Imbalance
source is highly imbalanced (93.9%)Imbalance
device_distinct_emails_8w is highly imbalanced (88.5%)Imbalance
bank_branch_count_8w has 144376 (14.4%) zerosZeros
month has 132440 (13.2%) zerosZeros

Reproduction

Analysis started2025-05-09 04:57:04.340196
Analysis finished2025-05-09 04:59:34.049752
Duration2 minutes and 29.71 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

fraud_bool
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
0
988971 
1
 
11029

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

Length

2025-05-09T13:59:34.275753image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:34.361787image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 988971
98.9%
1 11029
 
1.1%

income
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5626956
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:34.442754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.3
median0.6
Q30.8
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2903426
Coefficient of variation (CV)0.5159852
Kurtosis-1.2993664
Mean0.5626956
Median Absolute Deviation (MAD)0.2
Skewness-0.38633741
Sum562695.6
Variance0.084298826
MonotonicityNot monotonic
2025-05-09T13:59:34.669790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 221419
22.1%
0.1 157449
15.7%
0.8 146650
14.7%
0.6 111973
11.2%
0.7 105109
10.5%
0.4 81364
 
8.1%
0.2 69345
 
6.9%
0.5 55858
 
5.6%
0.3 50833
 
5.1%
ValueCountFrequency (%)
0.1 157449
15.7%
0.2 69345
 
6.9%
0.3 50833
 
5.1%
0.4 81364
 
8.1%
0.5 55858
 
5.6%
0.6 111973
11.2%
0.7 105109
10.5%
0.8 146650
14.7%
0.9 221419
22.1%
ValueCountFrequency (%)
0.9 221419
22.1%
0.8 146650
14.7%
0.7 105109
10.5%
0.6 111973
11.2%
0.5 55858
 
5.6%
0.4 81364
 
8.1%
0.3 50833
 
5.1%
0.2 69345
 
6.9%
0.1 157449
15.7%

name_email_similarity
Real number (ℝ)

Distinct998861
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49369409
Minimum1.4345505 × 10-6
Maximum0.99999932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:34.791756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.4345505 × 10-6
5-th percentile0.068085985
Q10.22521632
median0.49215253
Q30.75556731
95-th percentile0.91807338
Maximum0.99999932
Range0.99999788
Interquartile range (IQR)0.53035099

Descriptive statistics

Standard deviation0.2891248
Coefficient of variation (CV)0.58563552
Kurtosis-1.2802792
Mean0.49369409
Median Absolute Deviation (MAD)0.2651734
Skewness0.042839499
Sum493694.09
Variance0.08359315
MonotonicityNot monotonic
2025-05-09T13:59:34.920792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3893467318 2
 
< 0.1%
0.5330546849 2
 
< 0.1%
0.6703446771 2
 
< 0.1%
0.01269020396 2
 
< 0.1%
0.6218121045 2
 
< 0.1%
0.8279732618 2
 
< 0.1%
0.2955110628 2
 
< 0.1%
0.787399301 2
 
< 0.1%
0.7449692031 2
 
< 0.1%
0.5219805717 2
 
< 0.1%
Other values (998851) 999980
> 99.9%
ValueCountFrequency (%)
1.434550485 × 10-61
< 0.1%
1.015544817 × 10-51
< 0.1%
1.901656104 × 10-51
< 0.1%
1.93965659 × 10-51
< 0.1%
2.042122187 × 10-51
< 0.1%
2.230767456 × 10-51
< 0.1%
2.326447251 × 10-51
< 0.1%
2.51169962 × 10-51
< 0.1%
3.045742164 × 10-51
< 0.1%
3.198422688 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9999993178 1
< 0.1%
0.9999991158 1
< 0.1%
0.9999988877 1
< 0.1%
0.9999986598 1
< 0.1%
0.9999986427 1
< 0.1%
0.999998537 1
< 0.1%
0.9999984384 1
< 0.1%
0.9999983273 1
< 0.1%
0.9999981113 1
< 0.1%
0.999997937 1
< 0.1%

prev_address_months_count
Real number (ℝ)

HIGH CORRELATION 

Distinct374
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.718568
Minimum-1
Maximum383
Zeros0
Zeros (%)0.0%
Negative712920
Negative (%)71.3%
Memory size7.6 MiB
2025-05-09T13:59:35.046758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q312
95-th percentile98
Maximum383
Range384
Interquartile range (IQR)13

Descriptive statistics

Standard deviation44.04623
Coefficient of variation (CV)2.6345695
Kurtosis20.031089
Mean16.718568
Median Absolute Deviation (MAD)0
Skewness4.0638882
Sum16718568
Variance1940.0704
MonotonicityNot monotonic
2025-05-09T13:59:35.180792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 712920
71.3%
11 11475
 
1.1%
28 10275
 
1.0%
29 10150
 
1.0%
10 10082
 
1.0%
27 10007
 
1.0%
30 9362
 
0.9%
26 9036
 
0.9%
12 8712
 
0.9%
31 8526
 
0.9%
Other values (364) 199455
 
19.9%
ValueCountFrequency (%)
-1 712920
71.3%
5 1
 
< 0.1%
6 53
 
< 0.1%
7 409
 
< 0.1%
8 1962
 
0.2%
9 5558
 
0.6%
10 10082
 
1.0%
11 11475
 
1.1%
12 8712
 
0.9%
13 4448
 
0.4%
ValueCountFrequency (%)
383 1
 
< 0.1%
381 1
 
< 0.1%
377 3
< 0.1%
375 1
 
< 0.1%
374 1
 
< 0.1%
373 2
 
< 0.1%
372 1
 
< 0.1%
371 5
< 0.1%
370 2
 
< 0.1%
369 2
 
< 0.1%

current_address_months_count
Real number (ℝ)

HIGH CORRELATION 

Distinct423
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.587867
Minimum-1
Maximum428
Zeros9609
Zeros (%)1.0%
Negative4254
Negative (%)0.4%
Memory size7.6 MiB
2025-05-09T13:59:35.309794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile3
Q119
median52
Q3130
95-th percentile277
Maximum428
Range429
Interquartile range (IQR)111

Descriptive statistics

Standard deviation88.406599
Coefficient of variation (CV)1.0210045
Kurtosis1.3568646
Mean86.587867
Median Absolute Deviation (MAD)41
Skewness1.3869977
Sum86587867
Variance7815.7268
MonotonicityNot monotonic
2025-05-09T13:59:35.429791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 16376
 
1.6%
7 16270
 
1.6%
8 16089
 
1.6%
5 15896
 
1.6%
9 15688
 
1.6%
4 15030
 
1.5%
10 14739
 
1.5%
11 13988
 
1.4%
3 13986
 
1.4%
12 13063
 
1.3%
Other values (413) 848875
84.9%
ValueCountFrequency (%)
-1 4254
 
0.4%
0 9609
1.0%
1 11297
1.1%
2 12741
1.3%
3 13986
1.4%
4 15030
1.5%
5 15896
1.6%
6 16376
1.6%
7 16270
1.6%
8 16089
1.6%
ValueCountFrequency (%)
428 1
 
< 0.1%
425 2
< 0.1%
424 1
 
< 0.1%
419 1
 
< 0.1%
418 2
< 0.1%
417 1
 
< 0.1%
416 2
< 0.1%
414 1
 
< 0.1%
413 1
 
< 0.1%
412 4
< 0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.68908
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:35.526794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q120
median30
Q340
95-th percentile50
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.025799
Coefficient of variation (CV)0.35696429
Kurtosis-0.11520329
Mean33.68908
Median Absolute Deviation (MAD)10
Skewness0.47807881
Sum33689080
Variance144.61983
MonotonicityNot monotonic
2025-05-09T13:59:35.622800image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
30 311433
31.1%
20 245855
24.6%
40 238712
23.9%
50 140353
14.0%
60 34770
 
3.5%
10 20987
 
2.1%
70 6517
 
0.7%
80 1297
 
0.1%
90 76
 
< 0.1%
ValueCountFrequency (%)
10 20987
 
2.1%
20 245855
24.6%
30 311433
31.1%
40 238712
23.9%
50 140353
14.0%
60 34770
 
3.5%
70 6517
 
0.7%
80 1297
 
0.1%
90 76
 
< 0.1%
ValueCountFrequency (%)
90 76
 
< 0.1%
80 1297
 
0.1%
70 6517
 
0.7%
60 34770
 
3.5%
50 140353
14.0%
40 238712
23.9%
30 311433
31.1%
20 245855
24.6%
10 20987
 
2.1%

days_since_request
Real number (ℝ)

Distinct989330
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0257052
Minimum4.0368598 × 10-9
Maximum78.456904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:35.743793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4.0368598 × 10-9
5-th percentile0.0014230075
Q10.0071932463
median0.015175738
Q30.026330688
95-th percentile6.6822723
Maximum78.456904
Range78.456904
Interquartile range (IQR)0.019137441

Descriptive statistics

Standard deviation5.3818346
Coefficient of variation (CV)5.2469603
Kurtosis106.56921
Mean1.0257052
Median Absolute Deviation (MAD)0.0090633798
Skewness9.2789546
Sum1025705.2
Variance28.964144
MonotonicityNot monotonic
2025-05-09T13:59:35.870792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01396095638 3
 
< 0.1%
0.02850025384 3
 
< 0.1%
0.02850191589 3
 
< 0.1%
0.01875563927 3
 
< 0.1%
0.02283847732 3
 
< 0.1%
0.007416097159 3
 
< 0.1%
0.01580979536 3
 
< 0.1%
0.03372831864 3
 
< 0.1%
0.02584567607 3
 
< 0.1%
0.0185798607 3
 
< 0.1%
Other values (989320) 999970
> 99.9%
ValueCountFrequency (%)
4.036859789 × 10-91
< 0.1%
3.112790756 × 10-81
< 0.1%
4.284949667 × 10-81
< 0.1%
6.365528719 × 10-81
< 0.1%
6.467590399 × 10-81
< 0.1%
6.688882901 × 10-81
< 0.1%
8.406701996 × 10-81
< 0.1%
9.943647861 × 10-81
< 0.1%
1.414623874 × 10-71
< 0.1%
1.531941115 × 10-71
< 0.1%
ValueCountFrequency (%)
78.45690384 1
< 0.1%
76.58147717 1
< 0.1%
76.57726536 1
< 0.1%
76.29663468 1
< 0.1%
76.16477587 1
< 0.1%
75.98932696 1
< 0.1%
75.96965655 1
< 0.1%
75.8422922 1
< 0.1%
75.82975624 1
< 0.1%
75.8208081 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct994971
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6614985
Minimum-15.530555
Maximum112.95693
Zeros0
Zeros (%)0.0%
Negative742523
Negative (%)74.3%
Memory size7.6 MiB
2025-05-09T13:59:35.999767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-15.530555
5-th percentile-1.5880108
Q1-1.1814879
median-0.83050654
Q34.9841758
95-th percentile50.394003
Maximum112.95693
Range128.48748
Interquartile range (IQR)6.1656638

Descriptive statistics

Standard deviation20.236155
Coefficient of variation (CV)2.3363341
Kurtosis6.8466706
Mean8.6614985
Median Absolute Deviation (MAD)0.42598887
Skewness2.5071734
Sum8661498.5
Variance409.50195
MonotonicityNot monotonic
2025-05-09T13:59:36.134804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4690185647 3
 
< 0.1%
-0.7659779145 3
 
< 0.1%
-0.7027318049 3
 
< 0.1%
-1.189146437 3
 
< 0.1%
-0.8829498799 3
 
< 0.1%
-0.5040599226 3
 
< 0.1%
-1.543046823 3
 
< 0.1%
-1.532082595 3
 
< 0.1%
-0.7245396366 3
 
< 0.1%
-1.51428026 3
 
< 0.1%
Other values (994961) 999970
> 99.9%
ValueCountFrequency (%)
-15.53055484 1
< 0.1%
-15.38759018 1
< 0.1%
-15.16738779 1
< 0.1%
-14.5556558 1
< 0.1%
-14.38393244 1
< 0.1%
-14.29225918 1
< 0.1%
-14.0649795 1
< 0.1%
-13.90139264 1
< 0.1%
-13.70559412 1
< 0.1%
-13.20278612 1
< 0.1%
ValueCountFrequency (%)
112.9569277 1
< 0.1%
112.7561112 1
< 0.1%
112.4862322 1
< 0.1%
112.4520783 1
< 0.1%
112.3502013 1
< 0.1%
112.3251234 1
< 0.1%
112.253107 1
< 0.1%
112.2090762 1
< 0.1%
112.1350146 1
< 0.1%
111.9890434 1
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 MiB
AB
370554 
AA
258249 
AC
252071 
AD
118837 
AE
 
289

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd rowAD
3rd rowAB
4th rowAB
5th rowAA

Common Values

ValueCountFrequency (%)
AB 370554
37.1%
AA 258249
25.8%
AC 252071
25.2%
AD 118837
 
11.9%
AE 289
 
< 0.1%

Length

2025-05-09T13:59:36.255766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:36.346799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ab 370554
37.1%
aa 258249
25.8%
ac 252071
25.2%
ad 118837
 
11.9%
ae 289
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 1258249
62.9%
B 370554
 
18.5%
C 252071
 
12.6%
D 118837
 
5.9%
E 289
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1258249
62.9%
B 370554
 
18.5%
C 252071
 
12.6%
D 118837
 
5.9%
E 289
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1258249
62.9%
B 370554
 
18.5%
C 252071
 
12.6%
D 118837
 
5.9%
E 289
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1258249
62.9%
B 370554
 
18.5%
C 252071
 
12.6%
D 118837
 
5.9%
E 289
 
< 0.1%

zip_count_4w
Real number (ℝ)

Distinct6306
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1572.692
Minimum1
Maximum6700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:36.457799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile478
Q1894
median1263
Q31944
95-th percentile3677
Maximum6700
Range6699
Interquartile range (IQR)1050

Descriptive statistics

Standard deviation1005.3746
Coefficient of variation (CV)0.63926982
Kurtosis2.1399835
Mean1572.692
Median Absolute Deviation (MAD)462
Skewness1.4566566
Sum1.572692 × 109
Variance1010778
MonotonicityNot monotonic
2025-05-09T13:59:36.657801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1020 798
 
0.1%
1062 782
 
0.1%
1042 770
 
0.1%
969 767
 
0.1%
1026 763
 
0.1%
996 762
 
0.1%
1030 759
 
0.1%
1033 755
 
0.1%
1102 754
 
0.1%
1112 753
 
0.1%
Other values (6296) 992337
99.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 5
< 0.1%
3 6
< 0.1%
4 3
 
< 0.1%
5 11
< 0.1%
6 9
< 0.1%
7 5
< 0.1%
8 7
< 0.1%
9 6
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
6700 1
< 0.1%
6676 1
< 0.1%
6634 1
< 0.1%
6568 1
< 0.1%
6563 1
< 0.1%
6540 1
< 0.1%
6528 1
< 0.1%
6525 1
< 0.1%
6521 1
< 0.1%
6519 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct998687
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5665.2966
Minimum-170.60307
Maximum16715.565
Zeros0
Zeros (%)0.0%
Negative44
Negative (%)< 0.1%
Memory size7.6 MiB
2025-05-09T13:59:36.787802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-170.60307
5-th percentile1273.4489
Q13436.3658
median5319.7693
Q37680.7178
95-th percentile11234.63
Maximum16715.565
Range16886.168
Interquartile range (IQR)4244.352

Descriptive statistics

Standard deviation3009.3807
Coefficient of variation (CV)0.53119561
Kurtosis0.0029981024
Mean5665.2966
Median Absolute Deviation (MAD)2099.3659
Skewness0.56268209
Sum5.6652966 × 109
Variance9056372
MonotonicityNot monotonic
2025-05-09T13:59:36.915819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8506.533953 2
 
< 0.1%
4181.349372 2
 
< 0.1%
4407.300112 2
 
< 0.1%
3638.518699 2
 
< 0.1%
4366.049276 2
 
< 0.1%
3050.271915 2
 
< 0.1%
7639.524021 2
 
< 0.1%
3163.580104 2
 
< 0.1%
8996.185725 2
 
< 0.1%
4793.328741 2
 
< 0.1%
Other values (998677) 999980
> 99.9%
ValueCountFrequency (%)
-170.6030724 1
< 0.1%
-155.4307304 1
< 0.1%
-139.0967681 1
< 0.1%
-130.456928 1
< 0.1%
-125.4000058 1
< 0.1%
-123.4603293 1
< 0.1%
-122.0716816 1
< 0.1%
-110.8212233 1
< 0.1%
-106.9782971 1
< 0.1%
-101.557304 1
< 0.1%
ValueCountFrequency (%)
16715.5654 1
< 0.1%
16665.35953 1
< 0.1%
16640.74974 1
< 0.1%
16606.63885 1
< 0.1%
16605.49794 1
< 0.1%
16595.74824 1
< 0.1%
16579.10172 1
< 0.1%
16564.61032 1
< 0.1%
16556.02856 1
< 0.1%
16545.25413 1
< 0.1%

velocity_24h
Real number (ℝ)

HIGH CORRELATION 

Distinct998940
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4769.782
Minimum1300.3073
Maximum9506.8966
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:37.043771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1300.3073
5-th percentile2572.4986
Q13593.1791
median4749.9212
Q35752.5742
95-th percentile7348.9906
Maximum9506.8966
Range8206.5893
Interquartile range (IQR)2159.3951

Descriptive statistics

Standard deviation1479.2126
Coefficient of variation (CV)0.31012164
Kurtosis-0.37365389
Mean4769.782
Median Absolute Deviation (MAD)1083.1853
Skewness0.33113356
Sum4.769782 × 109
Variance2188070
MonotonicityNot monotonic
2025-05-09T13:59:37.161775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6676.20131 3
 
< 0.1%
5082.326632 3
 
< 0.1%
3906.416302 3
 
< 0.1%
4667.863854 3
 
< 0.1%
5768.904568 2
 
< 0.1%
4001.253496 2
 
< 0.1%
6171.510236 2
 
< 0.1%
3998.910295 2
 
< 0.1%
4736.615803 2
 
< 0.1%
5581.522791 2
 
< 0.1%
Other values (998930) 999976
> 99.9%
ValueCountFrequency (%)
1300.307314 1
< 0.1%
1320.283991 1
< 0.1%
1324.786832 1
< 0.1%
1326.966135 1
< 0.1%
1328.410255 1
< 0.1%
1348.830318 1
< 0.1%
1357.998349 1
< 0.1%
1358.465736 1
< 0.1%
1366.47556 1
< 0.1%
1366.485702 1
< 0.1%
ValueCountFrequency (%)
9506.896596 1
< 0.1%
9502.725577 1
< 0.1%
9489.970949 1
< 0.1%
9486.025796 1
< 0.1%
9483.255648 1
< 0.1%
9477.65335 1
< 0.1%
9472.491584 1
< 0.1%
9462.723773 1
< 0.1%
9460.446339 1
< 0.1%
9454.83697 1
< 0.1%

velocity_4w
Real number (ℝ)

HIGH CORRELATION 

Distinct998318
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4856.324
Minimum2825.7484
Maximum6994.7642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:37.286772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2825.7484
5-th percentile3118.1793
Q14268.3684
median4913.4369
Q35488.0834
95-th percentile6458.3201
Maximum6994.7642
Range4169.0158
Interquartile range (IQR)1219.7149

Descriptive statistics

Standard deviation919.84393
Coefficient of variation (CV)0.18941157
Kurtosis-0.35962526
Mean4856.324
Median Absolute Deviation (MAD)619.45681
Skewness-0.060124771
Sum4.856324 × 109
Variance846112.86
MonotonicityNot monotonic
2025-05-09T13:59:37.404809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5466.870949 3
 
< 0.1%
4297.664858 3
 
< 0.1%
5599.839956 2
 
< 0.1%
5604.141015 2
 
< 0.1%
4923.423307 2
 
< 0.1%
4370.319546 2
 
< 0.1%
4315.902988 2
 
< 0.1%
5478.557357 2
 
< 0.1%
4329.149466 2
 
< 0.1%
5449.383938 2
 
< 0.1%
Other values (998308) 999978
> 99.9%
ValueCountFrequency (%)
2825.748405 1
< 0.1%
2863.783336 1
< 0.1%
2864.909845 1
< 0.1%
2922.99389 1
< 0.1%
2925.966963 1
< 0.1%
2930.765607 1
< 0.1%
2935.463659 1
< 0.1%
2939.194903 1
< 0.1%
2941.133963 1
< 0.1%
2943.458414 1
< 0.1%
ValueCountFrequency (%)
6994.764201 1
< 0.1%
6988.564717 1
< 0.1%
6974.573281 1
< 0.1%
6966.17144 1
< 0.1%
6964.040026 1
< 0.1%
6961.616793 1
< 0.1%
6960.141498 1
< 0.1%
6957.582288 1
< 0.1%
6957.343895 1
< 0.1%
6956.810559 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2326
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.36185
Minimum0
Maximum2385
Zeros144376
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:37.527809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q325
95-th percentile1463
Maximum2385
Range2385
Interquartile range (IQR)24

Descriptive statistics

Standard deviation459.62533
Coefficient of variation (CV)2.493061
Kurtosis6.5029208
Mean184.36185
Median Absolute Deviation (MAD)8
Skewness2.7471608
Sum1.8436185 × 108
Variance211255.44
MonotonicityNot monotonic
2025-05-09T13:59:37.651808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 149028
 
14.9%
0 144376
 
14.4%
2 57792
 
5.8%
11 31160
 
3.1%
10 30694
 
3.1%
12 30537
 
3.1%
9 29239
 
2.9%
13 28182
 
2.8%
8 27015
 
2.7%
14 25698
 
2.6%
Other values (2316) 446279
44.6%
ValueCountFrequency (%)
0 144376
14.4%
1 149028
14.9%
2 57792
 
5.8%
3 17595
 
1.8%
4 14671
 
1.5%
5 17605
 
1.8%
6 21086
 
2.1%
7 24174
 
2.4%
8 27015
 
2.7%
9 29239
 
2.9%
ValueCountFrequency (%)
2385 1
< 0.1%
2381 1
< 0.1%
2380 1
< 0.1%
2371 1
< 0.1%
2367 1
< 0.1%
2359 1
< 0.1%
2355 1
< 0.1%
2351 2
< 0.1%
2350 1
< 0.1%
2349 1
< 0.1%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.503544
Minimum0
Maximum39
Zeros1336
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:37.768817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median9
Q313
95-th percentile19
Maximum39
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0337919
Coefficient of variation (CV)0.52967523
Kurtosis0.43644939
Mean9.503544
Median Absolute Deviation (MAD)4
Skewness0.70324987
Sum9503544
Variance25.339061
MonotonicityNot monotonic
2025-05-09T13:59:37.889983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7 83193
 
8.3%
5 80799
 
8.1%
8 79491
 
7.9%
6 78225
 
7.8%
11 73456
 
7.3%
9 67341
 
6.7%
10 62681
 
6.3%
4 62549
 
6.3%
13 60773
 
6.1%
12 52409
 
5.2%
Other values (30) 299083
29.9%
ValueCountFrequency (%)
0 1336
 
0.1%
1 14377
 
1.4%
2 35717
3.6%
3 44227
4.4%
4 62549
6.3%
5 80799
8.1%
6 78225
7.8%
7 83193
8.3%
8 79491
7.9%
9 67341
6.7%
ValueCountFrequency (%)
39 2
 
< 0.1%
38 3
 
< 0.1%
37 12
 
< 0.1%
36 31
 
< 0.1%
35 49
 
< 0.1%
34 85
 
< 0.1%
33 142
 
< 0.1%
32 223
< 0.1%
31 273
< 0.1%
30 383
< 0.1%

employment_status
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 MiB
CA
730252 
CB
138288 
CF
 
44034
CC
 
37758
CD
 
26522
Other values (2)
 
23146

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCB
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA

Common Values

ValueCountFrequency (%)
CA 730252
73.0%
CB 138288
 
13.8%
CF 44034
 
4.4%
CC 37758
 
3.8%
CD 26522
 
2.7%
CE 22693
 
2.3%
CG 453
 
< 0.1%

Length

2025-05-09T13:59:38.003984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:38.096984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
ca 730252
73.0%
cb 138288
 
13.8%
cf 44034
 
4.4%
cc 37758
 
3.8%
cd 26522
 
2.7%
ce 22693
 
2.3%
cg 453
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 1037758
51.9%
A 730252
36.5%
B 138288
 
6.9%
F 44034
 
2.2%
D 26522
 
1.3%
E 22693
 
1.1%
G 453
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1037758
51.9%
A 730252
36.5%
B 138288
 
6.9%
F 44034
 
2.2%
D 26522
 
1.3%
E 22693
 
1.1%
G 453
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1037758
51.9%
A 730252
36.5%
B 138288
 
6.9%
F 44034
 
2.2%
D 26522
 
1.3%
E 22693
 
1.1%
G 453
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1037758
51.9%
A 730252
36.5%
B 138288
 
6.9%
F 44034
 
2.2%
D 26522
 
1.3%
E 22693
 
1.1%
G 453
 
< 0.1%

credit_risk_score
Real number (ℝ)

HIGH CORRELATION 

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.9896
Minimum-170
Maximum389
Zeros524
Zeros (%)0.1%
Negative14445
Negative (%)1.4%
Memory size7.6 MiB
2025-05-09T13:59:38.204523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-170
5-th percentile28
Q183
median122
Q3178
95-th percentile255
Maximum389
Range559
Interquartile range (IQR)95

Descriptive statistics

Standard deviation69.681812
Coefficient of variation (CV)0.53196448
Kurtosis0.068087485
Mean130.9896
Median Absolute Deviation (MAD)46
Skewness0.29589538
Sum1.309896 × 108
Variance4855.555
MonotonicityNot monotonic
2025-05-09T13:59:38.323525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 6791
 
0.7%
116 6784
 
0.7%
110 6774
 
0.7%
115 6734
 
0.7%
117 6717
 
0.7%
114 6703
 
0.7%
109 6676
 
0.7%
105 6674
 
0.7%
112 6653
 
0.7%
111 6624
 
0.7%
Other values (541) 932870
93.3%
ValueCountFrequency (%)
-170 1
 
< 0.1%
-169 1
 
< 0.1%
-168 1
 
< 0.1%
-167 1
 
< 0.1%
-166 1
 
< 0.1%
-165 1
 
< 0.1%
-164 3
< 0.1%
-159 1
 
< 0.1%
-158 1
 
< 0.1%
-157 1
 
< 0.1%
ValueCountFrequency (%)
389 1
 
< 0.1%
387 1
 
< 0.1%
386 1
 
< 0.1%
385 2
 
< 0.1%
383 1
 
< 0.1%
381 1
 
< 0.1%
380 2
 
< 0.1%
379 2
 
< 0.1%
378 5
< 0.1%
377 6
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
1
529886 
0
470114 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

Length

2025-05-09T13:59:38.509228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:38.587229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

Most occurring characters

ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 529886
53.0%
0 470114
47.0%

housing_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.3 MiB
BC
372143 
BB
260965 
BA
169675 
BE
169135 
BD
 
26161
Other values (2)
 
1921

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBC
2nd rowBC
3rd rowBC
4th rowBC
5th rowBC

Common Values

ValueCountFrequency (%)
BC 372143
37.2%
BB 260965
26.1%
BA 169675
17.0%
BE 169135
16.9%
BD 26161
 
2.6%
BF 1669
 
0.2%
BG 252
 
< 0.1%

Length

2025-05-09T13:59:38.673229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:38.764466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
bc 372143
37.2%
bb 260965
26.1%
ba 169675
17.0%
be 169135
16.9%
bd 26161
 
2.6%
bf 1669
 
0.2%
bg 252
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 1260965
63.0%
C 372143
 
18.6%
A 169675
 
8.5%
E 169135
 
8.5%
D 26161
 
1.3%
F 1669
 
0.1%
G 252
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 1260965
63.0%
C 372143
 
18.6%
A 169675
 
8.5%
E 169135
 
8.5%
D 26161
 
1.3%
F 1669
 
0.1%
G 252
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 1260965
63.0%
C 372143
 
18.6%
A 169675
 
8.5%
E 169135
 
8.5%
D 26161
 
1.3%
F 1669
 
0.1%
G 252
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 1260965
63.0%
C 372143
 
18.6%
A 169675
 
8.5%
E 169135
 
8.5%
D 26161
 
1.3%
F 1669
 
0.1%
G 252
 
< 0.1%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
0
582923 
1
417077 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%

Length

2025-05-09T13:59:38.869458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:38.947491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%

Most occurring characters

ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 582923
58.3%
1 417077
41.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
1
889676 
0
110324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

Length

2025-05-09T13:59:39.033459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:39.112459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

Most occurring characters

ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 889676
89.0%
0 110324
 
11.0%

bank_months_count
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.839303
Minimum-1
Maximum32
Zeros0
Zeros (%)0.0%
Negative253635
Negative (%)25.4%
Memory size7.6 MiB
2025-05-09T13:59:39.198287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median5
Q325
95-th percentile30
Maximum32
Range33
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.116875
Coefficient of variation (CV)1.1178647
Kurtosis-1.4362302
Mean10.839303
Median Absolute Deviation (MAD)6
Skewness0.488747
Sum10839303
Variance146.81865
MonotonicityNot monotonic
2025-05-09T13:59:39.305259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-1 253635
25.4%
1 194802
19.5%
28 80082
 
8.0%
15 59141
 
5.9%
30 50777
 
5.1%
31 46084
 
4.6%
25 40450
 
4.0%
10 37158
 
3.7%
20 30850
 
3.1%
21 29098
 
2.9%
Other values (23) 177923
17.8%
ValueCountFrequency (%)
-1 253635
25.4%
1 194802
19.5%
2 25836
 
2.6%
3 8580
 
0.9%
4 3834
 
0.4%
5 28001
 
2.8%
6 17678
 
1.8%
7 931
 
0.1%
8 30
 
< 0.1%
9 4801
 
0.5%
ValueCountFrequency (%)
32 46
 
< 0.1%
31 46084
4.6%
30 50777
5.1%
29 11696
 
1.2%
28 80082
8.0%
27 4197
 
0.4%
26 24779
 
2.5%
25 40450
4.0%
24 1822
 
0.2%
23 320
 
< 0.1%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
0
777012 
1
222988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

Length

2025-05-09T13:59:39.418259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:39.502260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

Most occurring characters

ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 777012
77.7%
1 222988
 
22.3%

proposed_credit_limit
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.85101
Minimum190
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:39.582290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile200
Q1200
median200
Q3500
95-th percentile1500
Maximum2100
Range1910
Interquartile range (IQR)300

Descriptive statistics

Standard deviation487.5599
Coefficient of variation (CV)0.94515644
Kurtosis0.16883858
Mean515.85101
Median Absolute Deviation (MAD)0
Skewness1.30141
Sum5.1585101 × 108
Variance237714.66
MonotonicityNot monotonic
2025-05-09T13:59:39.675290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 613854
61.4%
1500 145735
 
14.6%
500 135797
 
13.6%
1000 81882
 
8.2%
990 7580
 
0.8%
510 6967
 
0.7%
2000 6114
 
0.6%
490 870
 
0.1%
210 607
 
0.1%
1900 390
 
< 0.1%
Other values (2) 204
 
< 0.1%
ValueCountFrequency (%)
190 163
 
< 0.1%
200 613854
61.4%
210 607
 
0.1%
490 870
 
0.1%
500 135797
 
13.6%
510 6967
 
0.7%
990 7580
 
0.8%
1000 81882
 
8.2%
1500 145735
 
14.6%
1900 390
 
< 0.1%
ValueCountFrequency (%)
2100 41
 
< 0.1%
2000 6114
 
0.6%
1900 390
 
< 0.1%
1500 145735
14.6%
1000 81882
8.2%
990 7580
 
0.8%
510 6967
 
0.7%
500 135797
13.6%
490 870
 
0.1%
210 607
 
0.1%

foreign_request
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
0
974758 
1
 
25242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

Length

2025-05-09T13:59:39.778291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:39.855264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 974758
97.5%
1 25242
 
2.5%

source
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 MiB
INTERNET
992952 
TELEAPP
 
7048

Length

Max length8
Median length8
Mean length7.992952
Min length7

Characters and Unicode

Total characters7992952
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINTERNET
2nd rowINTERNET
3rd rowINTERNET
4th rowINTERNET
5th rowINTERNET

Common Values

ValueCountFrequency (%)
INTERNET 992952
99.3%
TELEAPP 7048
 
0.7%

Length

2025-05-09T13:59:39.943044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:40.024043image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
internet 992952
99.3%
teleapp 7048
 
0.7%

Most occurring characters

ValueCountFrequency (%)
E 2000000
25.0%
T 1992952
24.9%
N 1985904
24.8%
I 992952
12.4%
R 992952
12.4%
P 14096
 
0.2%
L 7048
 
0.1%
A 7048
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7992952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2000000
25.0%
T 1992952
24.9%
N 1985904
24.8%
I 992952
12.4%
R 992952
12.4%
P 14096
 
0.2%
L 7048
 
0.1%
A 7048
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7992952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2000000
25.0%
T 1992952
24.9%
N 1985904
24.8%
I 992952
12.4%
R 992952
12.4%
P 14096
 
0.2%
L 7048
 
0.1%
A 7048
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7992952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2000000
25.0%
T 1992952
24.9%
N 1985904
24.8%
I 992952
12.4%
R 992952
12.4%
P 14096
 
0.2%
L 7048
 
0.1%
A 7048
 
0.1%

session_length_in_minutes
Real number (ℝ)

Distinct994887
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5449402
Minimum-1
Maximum85.899143
Zeros0
Zeros (%)0.0%
Negative2015
Negative (%)0.2%
Memory size7.6 MiB
2025-05-09T13:59:40.122086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.2538275
Q13.103053
median5.1143213
Q38.8661311
95-th percentile21.698864
Maximum85.899143
Range86.899143
Interquartile range (IQR)5.7630781

Descriptive statistics

Standard deviation8.0331064
Coefficient of variation (CV)1.0647011
Kurtosis14.961305
Mean7.5449402
Median Absolute Deviation (MAD)2.5800886
Skewness3.304575
Sum7544940.2
Variance64.530798
MonotonicityNot monotonic
2025-05-09T13:59:40.235262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 2015
 
0.2%
9.661988782 3
 
< 0.1%
7.960109353 3
 
< 0.1%
4.19239173 3
 
< 0.1%
2.46369682 3
 
< 0.1%
1.844220522 3
 
< 0.1%
5.101613717 3
 
< 0.1%
4.443947636 3
 
< 0.1%
4.638723019 3
 
< 0.1%
13.15259704 3
 
< 0.1%
Other values (994877) 997958
99.8%
ValueCountFrequency (%)
-1 2015
0.2%
0.0008720274159 1
 
< 0.1%
0.002297811351 1
 
< 0.1%
0.003262352826 1
 
< 0.1%
0.003563273337 1
 
< 0.1%
0.004286417425 1
 
< 0.1%
0.004588626771 1
 
< 0.1%
0.005553836113 1
 
< 0.1%
0.005618456469 1
 
< 0.1%
0.00579803797 1
 
< 0.1%
ValueCountFrequency (%)
85.89914319 1
< 0.1%
83.37677458 1
< 0.1%
83.21353581 1
< 0.1%
82.47861609 1
< 0.1%
82.29932743 1
< 0.1%
82.25407808 1
< 0.1%
82.03736106 1
< 0.1%
82.03581613 1
< 0.1%
82.00473129 1
< 0.1%
81.83908038 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.8 MiB
other
342728 
linux
332712 
windows
263506 
macintosh
53826 
x11
 
7228

Length

Max length9
Median length5
Mean length5.72786
Min length3

Characters and Unicode

Total characters5727860
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlinux
2nd rowother
3rd rowwindows
4th rowlinux
5th rowother

Common Values

ValueCountFrequency (%)
other 342728
34.3%
linux 332712
33.3%
windows 263506
26.4%
macintosh 53826
 
5.4%
x11 7228
 
0.7%

Length

2025-05-09T13:59:40.345265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:40.439264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
other 342728
34.3%
linux 332712
33.3%
windows 263506
26.4%
macintosh 53826
 
5.4%
x11 7228
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 660060
11.5%
i 650044
11.3%
n 650044
11.3%
w 527012
9.2%
h 396554
 
6.9%
t 396554
 
6.9%
e 342728
 
6.0%
r 342728
 
6.0%
x 339940
 
5.9%
u 332712
 
5.8%
Other values (7) 1089484
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5727860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 660060
11.5%
i 650044
11.3%
n 650044
11.3%
w 527012
9.2%
h 396554
 
6.9%
t 396554
 
6.9%
e 342728
 
6.0%
r 342728
 
6.0%
x 339940
 
5.9%
u 332712
 
5.8%
Other values (7) 1089484
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5727860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 660060
11.5%
i 650044
11.3%
n 650044
11.3%
w 527012
9.2%
h 396554
 
6.9%
t 396554
 
6.9%
e 342728
 
6.0%
r 342728
 
6.0%
x 339940
 
5.9%
u 332712
 
5.8%
Other values (7) 1089484
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5727860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 660060
11.5%
i 650044
11.3%
n 650044
11.3%
w 527012
9.2%
h 396554
 
6.9%
t 396554
 
6.9%
e 342728
 
6.0%
r 342728
 
6.0%
x 339940
 
5.9%
u 332712
 
5.8%
Other values (7) 1089484
19.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
1
576947 
0
423053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

Length

2025-05-09T13:59:40.537265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:40.616266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

Most occurring characters

ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 576947
57.7%
0 423053
42.3%

device_distinct_emails_8w
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
1
968067 
2
 
25302
0
 
6272
-1
 
359

Length

Max length2
Median length1
Mean length1.000359
Min length1

Characters and Unicode

Total characters1000359
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 968067
96.8%
2 25302
 
2.5%
0 6272
 
0.6%
-1 359
 
< 0.1%

Length

2025-05-09T13:59:40.705267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:40.789266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 968426
96.8%
2 25302
 
2.5%
0 6272
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 968426
96.8%
2 25302
 
2.5%
0 6272
 
0.6%
- 359
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 968426
96.8%
2 25302
 
2.5%
0 6272
 
0.6%
- 359
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 968426
96.8%
2 25302
 
2.5%
0 6272
 
0.6%
- 359
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 968426
96.8%
2 25302
 
2.5%
0 6272
 
0.6%
- 359
 
< 0.1%

device_fraud_count
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
0
1000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000000
100.0%

Length

2025-05-09T13:59:40.880267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T13:59:40.955271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000000
100.0%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.288674
Minimum0
Maximum7
Zeros132440
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2025-05-09T13:59:41.025280image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2099942
Coefficient of variation (CV)0.67200159
Kurtosis-1.1283322
Mean3.288674
Median Absolute Deviation (MAD)2
Skewness0.11239628
Sum3288674
Variance4.8840742
MonotonicityIncreasing
2025-05-09T13:59:41.116267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 150936
15.1%
2 136979
13.7%
0 132440
13.2%
4 127691
12.8%
1 127620
12.8%
5 119323
11.9%
6 108168
10.8%
7 96843
9.7%
ValueCountFrequency (%)
0 132440
13.2%
1 127620
12.8%
2 136979
13.7%
3 150936
15.1%
4 127691
12.8%
5 119323
11.9%
6 108168
10.8%
7 96843
9.7%
ValueCountFrequency (%)
7 96843
9.7%
6 108168
10.8%
5 119323
11.9%
4 127691
12.8%
3 150936
15.1%
2 136979
13.7%
1 127620
12.8%
0 132440
13.2%

Interactions

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2025-05-09T13:59:16.436282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:20.193337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:23.825633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:27.677575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:25.339905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:29.043897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:32.763547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:36.366537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:40.067563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:43.749586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:47.286643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:51.006634image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:54.713695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:58.381683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:01.927840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:05.573934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:09.228773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:12.999174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:16.631319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:20.382340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:24.019669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:27.877576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:25.533903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:29.244895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:32.958515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:36.560572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:40.261596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:43.943587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:47.477643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:51.196635image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:54.909659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:58.571684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:02.132810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:05.780966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:09.437742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:13.202209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:16.829315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:20.578343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:24.204669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:28.078578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:25.736903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:29.442930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:33.152516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:36.770542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:40.459564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:44.134589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:47.671612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:51.405672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:55.111661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:58.767685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:02.323814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:05.976969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:09.640745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:13.400211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:17.037322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:20.781348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:24.396669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:28.278582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:25.940911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:29.647932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:33.364518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:36.986543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:40.663566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:44.332590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:47.872648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:51.616638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:55.312662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:58.969687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:02.529846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:06.172940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:09.864747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:13.601176image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:17.236318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:21.000344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:24.601668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:28.479584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:26.126906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:29.846935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:33.574552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:37.177543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:40.861568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:44.528591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:48.072615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:51.818640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:55.514666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:59.157721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:02.720814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:06.368939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:10.074772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:13.799209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:17.432319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:21.193347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:24.795673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:28.677582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:26.317907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:30.056902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:33.778521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:37.373544image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:41.060570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:44.724625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:48.259617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:52.026641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:55.724665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:59.347689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:02.919816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:06.575940image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:10.270806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:14.000178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:17.627324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:21.385348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:25.073644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:28.880583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:26.516908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:30.264902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:33.988523image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:37.576545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:41.272605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:44.928594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:48.460618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:52.223642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:55.930699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:59.547692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:03.127816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:06.779943image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:10.488878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:14.197181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:17.836324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:21.609344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:25.259676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:29.074584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:26.712879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:30.458935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:34.177555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:37.842580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:41.466571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:45.120595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:48.649618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:52.417677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:56.122668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:58:59.742692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:03.324817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:06.968976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:10.690879image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:14.388181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:18.039324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:21.807348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-09T13:59:25.450677image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-09T13:59:41.295269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
bank_branch_count_8wbank_months_countcredit_risk_scorecurrent_address_months_countcustomer_agedate_of_birth_distinct_emails_4wdays_since_requestdevice_distinct_emails_8wdevice_osemail_is_freeemployment_statusforeign_requestfraud_boolhas_other_cardshousing_statusincomeintended_balcon_amountkeep_alive_sessionmonthname_email_similaritypayment_typephone_home_validphone_mobile_validprev_address_months_countproposed_credit_limitsession_length_in_minutessourcevelocity_24hvelocity_4wvelocity_6hzip_count_4w
bank_branch_count_8w1.0000.507-0.0300.0500.041-0.0150.0140.0110.0260.0100.0210.0040.0180.0660.0260.0240.1660.009-0.051-0.0180.1370.0730.025-0.0710.0140.0050.0160.0460.0460.0260.017
bank_months_count0.5071.000-0.0520.0840.013-0.014-0.0060.0210.0350.0190.0550.0140.0250.0400.0470.0100.2080.031-0.033-0.0130.2880.0770.028-0.080-0.0050.0190.0330.0270.0340.0220.053
credit_risk_score-0.030-0.0521.0000.0830.166-0.135-0.0910.0340.0650.0400.0580.0160.0860.1430.1340.1810.0130.0470.1920.0480.0520.0210.029-0.0290.631-0.0420.015-0.156-0.199-0.151-0.104
current_address_months_count0.0500.0840.0831.0000.163-0.204-0.0260.0190.0530.0900.0580.0250.0570.0750.186-0.0300.1130.063-0.0100.0550.0610.1330.091-0.6530.115-0.0600.0190.0030.0070.0230.068
customer_age0.0410.0130.1660.1631.000-0.4420.0100.0350.0800.0470.1710.0150.0670.1290.1780.142-0.0210.0660.014-0.0470.0470.2160.155-0.1430.1520.0140.030-0.019-0.014-0.027-0.025
date_of_birth_distinct_emails_4w-0.015-0.014-0.135-0.204-0.4421.000-0.0420.0330.0450.0610.1340.0340.0560.0570.085-0.066-0.0330.051-0.2330.0240.0860.1840.1120.142-0.052-0.0290.0270.1510.2270.1150.136
days_since_request0.014-0.006-0.091-0.0260.010-0.0421.0000.0130.0130.0190.0120.0070.0060.0550.026-0.022-0.0340.002-0.022-0.0280.0750.0470.0100.024-0.0750.0540.0170.0460.0220.064-0.034
device_distinct_emails_8w0.0110.0210.0340.0190.0350.0330.0131.0000.0370.0080.0370.0090.0470.0280.0250.0120.0230.1160.0490.0190.0440.0130.0620.0110.0250.0460.4100.0330.0480.0250.019
device_os0.0260.0350.0650.0530.0800.0450.0130.0371.0000.1540.0590.0570.0800.0410.0760.0380.0550.0650.0560.0420.0630.0620.0810.0250.0600.0350.0760.0310.0530.0370.020
email_is_free0.0100.0190.0400.0900.0470.0610.0190.0080.1541.0000.0160.0290.0280.0320.0900.0250.0180.0230.0760.0740.0300.0140.0320.0240.0500.0430.0000.0550.0630.0490.031
employment_status0.0210.0550.0580.0580.1710.1340.0120.0370.0590.0161.0000.0210.0400.0450.1080.0590.0360.0620.0520.0390.0570.1420.1430.0260.0560.0260.0370.0400.0490.0350.035
foreign_request0.0040.0140.0160.0250.0150.0340.0070.0090.0570.0290.0211.0000.0170.0010.0270.0120.0130.0150.0370.0260.0250.0030.0030.0170.0290.0130.0080.0240.0390.0080.025
fraud_bool0.0180.0250.0860.0570.0670.0560.0060.0470.0800.0280.0400.0171.0000.0350.1150.0560.0330.0500.0180.0450.0390.0350.0130.0280.1080.0160.0040.0160.0190.0180.008
has_other_cards0.0660.0400.1430.0750.1290.0570.0550.0280.0410.0320.0450.0010.0351.0000.0810.0870.1230.0980.0950.0330.1450.1070.0050.0430.1290.1150.0190.0640.0890.0440.058
housing_status0.0260.0470.1340.1860.1780.0850.0260.0250.0760.0900.1080.0270.1150.0811.0000.0880.0710.0660.0700.0500.0980.1000.0900.0550.1340.0280.0240.0410.0650.0450.037
income0.0240.0100.181-0.0300.142-0.066-0.0220.0120.0380.0250.0590.0120.0560.0870.0881.0000.0990.0440.119-0.0390.0310.0250.0150.0160.135-0.0780.011-0.108-0.116-0.099-0.079
intended_balcon_amount0.1660.2080.0130.113-0.021-0.033-0.0340.0230.0550.0180.0360.0130.0330.1230.0710.0991.0000.025-0.0560.0630.4360.0270.052-0.0470.0960.0490.0140.0710.0690.0360.013
keep_alive_session0.0090.0310.0470.0630.0660.0510.0020.1160.0650.0230.0620.0150.0500.0980.0660.0440.0251.0000.1210.0380.0330.0420.0230.0430.0650.0480.0840.0490.1120.0350.048
month-0.051-0.0330.192-0.0100.014-0.233-0.0220.0490.0560.0760.0520.0370.0180.0950.0700.119-0.0560.1211.000-0.0400.0910.1060.056-0.011-0.012-0.1090.023-0.555-0.854-0.414-0.288
name_email_similarity-0.018-0.0130.0480.055-0.0470.024-0.0280.0190.0420.0740.0390.0260.0450.0330.050-0.0390.0630.038-0.0401.0000.0700.0310.038-0.0380.0750.0230.0120.0310.0500.0250.021
payment_type0.1370.2880.0520.0610.0470.0860.0750.0440.0630.0300.0570.0250.0390.1450.0980.0310.4360.0330.0910.0701.0000.0780.0570.0430.0580.0290.0620.0690.0890.0690.064
phone_home_valid0.0730.0770.0210.1330.2160.1840.0470.0130.0620.0140.1420.0030.0350.1070.1000.0250.0270.0420.1060.0310.0781.0000.2720.0440.0430.0470.0120.0610.0970.0500.079
phone_mobile_valid0.0250.0280.0290.0910.1550.1120.0100.0620.0810.0320.1430.0030.0130.0050.0900.0150.0520.0230.0560.0380.0570.2721.0000.0220.0290.0160.0230.0310.0540.0240.029
prev_address_months_count-0.071-0.080-0.029-0.653-0.1430.1420.0240.0110.0250.0240.0260.0170.0280.0430.0550.016-0.0470.043-0.011-0.0380.0430.0440.0221.000-0.0360.0830.0120.0230.0200.000-0.074
proposed_credit_limit0.014-0.0050.6310.1150.152-0.052-0.0750.0250.0600.0500.0560.0290.1080.1290.1340.1350.0960.065-0.0120.0750.0580.0430.029-0.0361.000-0.0000.013-0.0100.004-0.049-0.023
session_length_in_minutes0.0050.019-0.042-0.0600.014-0.0290.0540.0460.0350.0430.0260.0130.0160.1150.028-0.0780.0490.048-0.1090.0230.0290.0470.0160.083-0.0001.0000.0390.0910.1160.0650.050
source0.0160.0330.0150.0190.0300.0270.0170.4100.0760.0000.0370.0080.0040.0190.0240.0110.0140.0840.0230.0120.0620.0120.0230.0120.0130.0391.0000.0140.0220.0150.017
velocity_24h0.0460.027-0.1560.003-0.0190.1510.0460.0330.0310.0550.0400.0240.0160.0640.041-0.1080.0710.049-0.5550.0310.0690.0610.0310.023-0.0100.0910.0141.0000.5450.4730.217
velocity_4w0.0460.034-0.1990.007-0.0140.2270.0220.0480.0530.0630.0490.0390.0190.0890.065-0.1160.0690.112-0.8540.0500.0890.0970.0540.0200.0040.1160.0220.5451.0000.4050.293
velocity_6h0.0260.022-0.1510.023-0.0270.1150.0640.0250.0370.0490.0350.0080.0180.0440.045-0.0990.0360.035-0.4140.0250.0690.0500.0240.000-0.0490.0650.0150.4730.4051.0000.149
zip_count_4w0.0170.053-0.1040.068-0.0250.136-0.0340.0190.0200.0310.0350.0250.0080.0580.037-0.0790.0130.048-0.2880.0210.0640.0790.029-0.074-0.0230.0500.0170.2170.2930.1491.000

Missing values

2025-05-09T13:59:29.372618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-09T13:59:30.824596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
000.30.986506-125400.006735102.453711AA105913096.0350187850.9550076742.08056155CB1631BC01901500.00INTERNET16.224843linux1100
100.80.617426-189200.010095-0.849551AD16589223.2834315745.2514815941.664859318CA1541BC11201500.00INTERNET3.363854other1100
200.80.996707914400.012316-1.490386AB10954471.4721495471.9889585992.5551131511CA891BC01300200.00INTERNET22.730559windows0100
300.60.4751001114300.006991-1.863101AB348314431.9936216755.3444795970.3368311113CA901BC0110200.00INTERNET15.215816linux1100
400.90.842307-129405.74262647.152498AA23397601.5115795124.0469305940.73421216CA910BC11260200.00INTERNET3.743048other0100
500.60.294840-1369300.024232-1.232556AD120411556.9555147506.9512766482.9240377055CB1341BE11300200.00INTERNET6.987316linux1100
600.20.773085224400.006919-0.544676AB199811723.9936067864.2771446338.799156288CA721BC1110200.00INTERNET28.199923x111100
700.80.153880-1103400.045122-1.101184AB15484999.5558014526.8616676426.79081767CA1630BE11251200.00INTERNET11.234264other1100
800.30.523655212300.035206-0.955737AB17816979.9940024335.6853466624.957942210CA350BC1020200.00INTERNET5.329387other1100
900.80.834475-1134200.017245-1.356393AD31137549.9920866273.9221106312.9988351420CA2011BD111501500.00INTERNET4.103970other1100
fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
99999000.70.129088-1111300.029064-1.123643AB1450793.3033142341.4926933036.35167874CA2660BB01101500.00INTERNET3.481792linux1107
99999100.60.730618-1362400.02214396.775940AB6695267.3121302213.4919253028.221403414CA3441BC012701500.00INTERNET5.049117other1107
99999200.90.432945550300.015941-0.652537AB3844001.0799183624.9419843106.809780816CA1271BC01280200.00INTERNET1.400871linux1107
99999300.80.5326611215200.016189-0.694345AC4034099.4813703296.8171283134.518780912CA2480BF11-101500.00INTERNET5.755127other1107
99999400.90.046317-1132500.035818-1.504382AD5762948.1053412272.8222873112.8329447441CA1851BA0110200.00INTERNET3.632310linux0107
99999500.80.124690-1143300.051348-0.826239AB5306732.6024143010.0480993095.754245428CA3051BB113101500.00INTERNET16.967770other0107
99999600.90.824544-1193300.0095910.008307AC4081574.2932942716.4957674286.08905005CA2350BA11-111000.00INTERNET1.504109macintosh0107
99999700.80.140891-1202100.05928750.609995AA7491258.8649383601.3228923103.89166423CA1951BE01310200.00INTERNET16.068595other0107
99999800.90.002480523300.023357-1.313387AB7077048.1371286521.3950123068.26508478CA1480BD0110200.00INTERNET1.378683linux1107
99999900.60.993391-1174300.02042214.942456AA6553737.0764793135.7880943051.003293148CA1001BB01151200.00INTERNET1.947926other1107